In the design and evaluation of complex dynamic systems, such as vehicles and vehicle components, it is desirable and often necessary to test and tune the components. This is to determine the effect of the vehicle component on vehicle performance, and the effect of the vehicle on the component. Durability testing may be performed as well as other types of tests that are desired. A number of different methodologies and systems have been employed in the testing of components in vehicles.
FIGS. 1-4 show a data collection system and method for a conventional laboratory simulation test. In such a method, a physical vehicle 10 is driven over a test road 12 and the specific component responses are measured. For example, the displacement of a suspension strut (not shown) installed in the physical vehicle 10 can be measured and stored in an appropriate database. These responses are provided as reference numeral 14. The responses of the specific component, such as the strut, are used as reference measurements for the test control process.
Referring now to FIG. 2, a generic (i.e., random amplitude, broadband frequency) drive 16, also referred to here as a random rig drive, is input into a test rig 18. The specific vehicle component, in this example a suspension strut 20, is installed in the test rig 18. A rig controller 22 converts the drive signal from the random rig drive 16 to control the movement of the test rig 18. A response of the test component, such as a displacement of the strut 20, is measured at the test rig 18. The measurements are provided at 24 to form the test component response. In the example of FIG. 2, the test component response 24 is a random rig displacement in response to the input and the rig 16. The inputting of the random rig drive 16 and the measurement of the random rig displacement 24 are real-time processes. The rig controller 22 need not be a complex tracking controller as it merely responds to the random drive 16. The rig controller 22 does not perform complex real-time modeling calculations to compensate for rig or specimen dynamics.
The test component response 24 is used with the test rig drive 16 to calculate a general system dynamic response model 26. The response model represents the coupled dynamics of the test system and component. In a multi-input-multi-output test it would also represent the cross-coupled dynamics between control inputs. The response model 26, typically a frequency response function (FRF), will be inverted, and used for test rig drive prediction in the simulation control process. In this example, the determination of the general system dynamic response model 26 is an off-line process, since the entire drive and response time histories are required to calculate a well-defined FRF.
Hence, in the conventional test system and process, the first step is to determine the input/output relationship that exists in the laboratory at the test rig 18. The relationship between the inputs to the control system for the test and how that system responds to those inputs needs to be understood. With this understanding, a compensated test drive signal can be developed to generate any desired component response.
Following the determination of how the components respond in a vehicle environment (see FIG. 1); and how the test environment influences the component response (see FIG. 2), an iterative test drive signal development process is then performed, as depicted in FIG. 3.
In an initial iteration (N=0), the test rig response is considered to be zero, and the desired response 32, which was already determined in FIG. 1, is used with an inverse (FRF−1) 40 of the general system dynamic response model 26 determined in FIG. 2. to create an initial drive. In each iteration, a current test rig response 30 is compared to the desired response A comparator 34 provides the simulation error to generate a drive correction 38 using the inverse (FRF−1). At this time, the iteration number is incremented.
The drive correction 38 is added to a previous test rig drive 40 to generate a next test rig drive 42. The determination of the next test rig drive in response to the previous test rig response is an off-line process.
The next test rig drive 42 is applied to the test rig 18 and the component response 30 is measured. The process of FIG. 3 is repeated iteratively until the resulting simulation error is reduced below a desired tolerance value. In performing test drive iteration, the test rig drive 42 is incrementally changed to obtain the response from the test rig 18 that had been previously measured. In other words, a test rig drive 42 is determined that will produce the same response from the physical vehicle component that was previously obtained during the data collection phase of FIG. 1.
Once the test rig drive 42 is determined through the iterative process until the simulation error is below a predetermined value, this now-final test rig drive 44 is used for subsequent testing of the component, as seen in FIG. 4. Different types of testing can be performed, such as a performance test, durability test, etc.
While the conventional iterative test method has certain benefits, it is a requirement for this method to secure a desirable vehicle, apply instrumentation and acquire test data before preparing the test. This makes the conventional simulation test system and method less useful in certain respects. It is possible that a suitable test vehicle to measure the component response cannot be obtained prior to the need to test the vehicle component. For example, it may be desired to determine the response of a vehicle component of a vehicle that does not yet exist, such as a new model car that is not yet in production or even prototyped. Further, there is often insufficient time or resources to properly prepare a vehicle to measure data for a physical component test. Further, a large number of component variations may need to be tested, and each variation would affect the component response in the vehicle. Also, a component's response within the vehicle system often changes gradually over time, such as in a durability test, and testing must be adapted for the test to remain valid.
FIG. 5, depicts another system and methodology for testing a physical component, and is known as real-time mHIL (model hardware in loop). As opposed to the test method depicted in FIGS. 1-4, a physical vehicle is not driven over the road with the installed key component. Instead, a virtual vehicle is used, without the key component installed, and is driven over a virtual road. This vehicle is modeled by a processor (not shown). The vehicle model, represented as reference numeral 50, excludes the physical test component. The vehicle model generates a response 52 from being driven over the virtual test road. This response 52 is applied through a reflected-memory processor link 54 as a control input 56 to a physical test system, such as a test rig 58.
The test rig 58 includes a complex rig controller 60 in which a model is provided. Whatever happens inside the virtual vehicle needs to happen to the physical component 62 within the test rig 58. Hence, the test rig 58 includes the physical test component that was not provided in the vehicle model 50.
The response of the physical component 62 in the test rig 58 is provided as an additional input 64 to the model of the vehicle 50. This response is provided to the model 50 in real time via the reflected-memory link 54.
The real-time mHIL process depicted in FIG. 5 is a closed-loop process that allows the physical component test response to be evaluated immediately, and automatically adapt to changes in the test environment. Limitations of the application of this system are the fidelity of the real-time vehicle model, the speed of the reflected-memory link and processors, and the tracking performance of the test rig controller 60. In order for such a system to work, the model has to operate in real-time. To accomplish this with today's technology, the vehicle model and the modeling in the rig controller may have to be simplified. Also, real-time capable models may lack fidelity at higher frequencies, but an engineer evaluating durability may require simulation of these frequencies to achieve an accurate test. Hence, the real-time mHIL process and arrangement of FIG. 5 have constraints that can limit the usefulness of such a system.